Marc Fleischmann

I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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Quantinuum’s planned IPO is the moment quantum finally hits the public markets in grown‑up form. This isn’t another early‑stage SPAC story; it’s a full‑stack platform spun out of Honeywell, with trapped‑ion hardware that regularly tops industry benchmarks and a real software and applications business on top. By combining industrial‑grade engineering with production‑grade tooling in chemistry, security, and optimization, Quantinuum gives investors exposure not just to “qubits,” but to an integrated quantum stack that already serves enterprise workloads. For technical and business leaders, as well as investors, the key question now is how public markets will price a quantum company that actually looks like an infrastructure and software provider rather than a speculative science project. Whatever the final valuation, this IPO will likely become the benchmark for what “mature” looks like in quantum—deep IP, differentiated hardware, and diversified revenue experiments before ringing the bell. Headline: Honeywell’s Quantinuum Quantum-Computing Unit Plans IPO – https://lnkd.in/euZjkGtG

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Cerebras is now a generational AI company. But between 2018 and 2019, one tiny component nearly killed the company. Back then we were deep into building the first wafer-scale system. Everything depended on a single part most people will never notice. The interposer. It sits between the main board and the dinner-plate-sized silicon wafer. It has to carry enormous power and data loads while having to survive massive thermal cycling. Cold to hot. Off to fully lit. Over and over again. We had just built the largest chip in the world. There was no existing supply chain for it so we had to single source this part. That alone is terrifying. One vendor, no backup. If it failed, the system failed. If the system failed, the company could have failed. For almost three years, this thing was the boogeyman in every board meeting. Would it maintain electrical continuity? Would it warp under load? Would it survive burn-in? Would it work at scale? For long stretches, the honest answer was “we don’t know.” We were burning millions of dollars per month. Time was not our friend. A redesign could have pushed us far enough back that the market might have moved on. At one point, we pulled in a group that I first worked with at IDEO and that used to be called "Failure Analysis". That is not who you call when things are going well! We went back to first principles. Physics. Materials science. Coefficients of thermal expansion. Power dissipation per square millimeter. Analyzing what happens when you push this much power through a system at these temperatures. Again and again. Slowly, painfully, we got there. Finally the interposer worked. Every company pushing the frontier runs into problems like this. There are rarely quick, clever fixes. You just have to do real failure analysis, over and over, until the answer starts to reveal itself. The Cerebras team is itself built from the strongest, toughest material I’ve seen. They earned every inch of the other side of that problem.

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Big news from the Gray Line portfolio — I’m thrilled to announce our lead investment in Archive Intel's $6.3M Series A! Compliance has historically been a tax on innovation. Archive Intel flips that script. Their AI-powered platform consolidates archiving, marketing review, and supervision — cutting manual review by up to 85% and reducing false flags by 99%. More than 500 wealth management and financial services firms and 7,000 users are already on board. Under Larry Shumbres's leadership, the team is redefining how regulated firms operate — not by layering on more tools, but by finally unifying compliance into one intelligent system. We’re proud to partner alongside Social Leverage and Garuda Ventures as Archive Intel scales into the category leader it’s clearly on track to become. https://lnkd.in/gqsTPQhA

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The current-cycle AI infrastructure boom is unprecedented. The next cycle must be defined by AI creating real economic value. In barely a year, AI infra has become a huge part of US GDP growth: → Hyperscalers racing to build data-center capacity → NVIDIA racks pulling 120kW per unit → Hundreds of billions in CapEx committed over the next 24–36 months Soon, the market will start pricing in this fact: None of this infrastructure pays back unless AI delivers measurable ROI in the real world. Not “assistants” or "co-pilots". Not enterprise tire-kicking. Not flashy demoware that falls short at the hardest 10% of the job. Not productivity theater. Hard, real-world, financial outcomes. And this is why the next decade of AI alpha won’t be found in broad enterprise AI, it'll be in vertical AI with direct cost removal and / or revenue lift. This week on VERTICALS, when we spoke with Mike Kopko (CEO, Pearl Health), the pattern became obvious: → Avoiding a $30k hospitalization = immediate ROI → Aligning with value-based care = massive opportunity + society problem → Government-backed payment models = durable revenue Healthcare AI has potential to do more than "improve workflow." It has promise to make our HC system more efficient, profitable, and effective. That's the kind of economic impact that absorbs AI infra, rather than speculating on it. The 1st Generation of AI companies benefitted from novelty. But now, the question is shifting from: + “What's the biggest TAM we can slap AI onto" to: + "Which real-world workflows have clear AI-addressable cost removal / revenue uplift opportunity?" Healthcare is emerging as one of the strongest answers. 🎧 Full episode with Mike Kopko / Pearl Health here: https://lnkd.in/gMZnVvgq 📖 And our write-up on the AI infra cycle: https://lnkd.in/gPk9wb-q TLDR: Data-center CapEx won't be justified by copilots, it'll be justified by Vertical AI driving tangible ROI in risk, revenue, and cost.

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Garry Tan recently mentioned that forward deployed engineers at Palantir are the best candidates for starting vertical AI companies, and I completely agree. The deep customer engagement and knowledge about opaque enterprise workflows is crucial in this case. And guess who has been forward deploying engineers in US enterprises for the last 40 years? The IT services industry in India. This deep knowledge about enterprise workflows combined with an extensive bench of entrepreneurial talent in India makes India a fantastic source of vertical AI companies. We are already seeing this happen and will see more and more of this in the coming decade.

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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As AI workloads eat up global computing supply, DRAM prices are surging like never before—creating major challenges for teams trying to plan out their data center spend. One of my portfolio companies, Mext, is addressing the largest cost component in the datacenter: server memory (DRAM). From being on the board of Redis for the last twelve years, I’ve learned a lot about the various aspects of this problem and approaches to solving them. Mext found a breakthrough, using new AI techniques, for dramatically reducing the amount of server-DRAM required to run applications, all while maintaining performance. It intelligently manages the server’s memory, keeping hot pages (i.e., those in use by applications) in fast DRAM and offloading cold pages (i.e., those less used) to a much less expensive memory tier (e.g., NVMe Flash). Key to the approach is ensuring cold memory pages that are about to be accessed by an application are back in DRAM before the application needs them or notices that they were gone. This is done without modifications to the application or the OS – so it can run in the cloud or on-premise. Mext thus allows applications to either run using less DRAM or keep their DRAM footprint but do more with it. They're hosting a webinar along with Fred Weber (former CTO of AMD) on Jan 22nd at 10am pacific time. You can register here: https://lnkd.in/gXe9GCeE

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Not all KV cache solutions are built the same - some are virtually unusable add-ons (VUA for short) that are more marketing than engineering. NVIDIA Project Dynamo and the open source community are setting the bar for what performance should be. I am happy to say that WEKA is delivering memory class performance that can actually keep GPUs sufficiently saturated to feed not just today's but also tomorrow's AI demands. WEKA’s Val Bercovici explains what makes the difference. https://lnkd.in/gwqJgvy6

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